I think science often takes the requirement that criticism be constructive too far. Most definitions of constructive criticism refer to feedback that is intended to help by pointing out flaws in a way that is specific, actionable, and respectful. This is good, I’m in favor of it. However, in practice, the demands for constructive criticism in science go much further: frequently, we are told that, to publicly point out serious flaws, you must also solve them, and to do otherwise is somehow rude, unscholarly, or low effort.
The natural selection of bad science
Abstract. Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, sugg
Guest post: If you’re going to critique science, be scientific about it
Loren K. Mell Editor’s note: This post responds to a Feb. 13 article in The Atlantic, “The Scientific Literature Can’t Save Us Now,” written by Retraction Watch cofounders Adam Marcus and Ivan Oran…
What Does Constructive Mean?
How our “bridging” algorithms actually work, and our future plans.
Andreas De Block on Twitter / X
It took Nature three years to publish this rebuttal, which clearly demonstrates the fundamental flaws in the original paper. In the meantime, the paper's conclusions influenced scientific debate and policy decisions. Such delays in correcting flawed research do real damage. https://t.co/QI3Ei3PE70— Andreas De Block (@DeblockBlock) August 13, 2026
How To Think Real Good | Meta-rationality
Practical epistemology: heuristics for how to think about difficult things, particularly using formal methods.
Are Scientific Papers Bad?
No. 023: Co-Scientist, Terminal-Bench Science, AI in peer review - The Hallway Track
The Hallway Track No. 023 - How AI is affecting the practice of science
AI, peer review and the human activity of science
When researchers cede their scientific judgement to machines, we lose something important.
In Dropping Reflecting Pool Case, Pirro Draws Trump’s Wrath
Jeanine Pirro, the U.S. attorney in Washington, blamed shoddy construction, contradicting President Trump’s pet theory. Mr. Trump said he disagreed “100%” with her.
Illusions of Understanding in the Sciences
Scientists seek to understand the causes of observed phenomena. Beliefs that they have succeeded are based on understanding that is rarely or possibly never complete, and varies in depth and quality. Most often scientists believe they understand more than they do, making their belief an illusion. This illusion then persists in explanations scientists provide in print, in talks, or in discussions. The illusion that a scientist has a valid and complete explanation tends to be magnified when the data are well described by mathematical and computer simulation models due to the precision of such models and their ability to predict well; prediction does not imply causality, but gives the illusion that it does. The first part of this essay supports the case for the universality of partial and incomplete levels of understanding by showing the difficulty of reaching a deep level of understanding for even a simple analysis and model that most scientists use and believe they understand: linear regression. The second part highlights some implications of the existence of many levels of understanding and explanation, and their use by scientists for design, testing, analysis, and theory development. It discusses the way that deduction and induction depend on the levels of understanding and the implications of the illusion that a scientist’s understanding is deep. It makes a case that the many incomplete levels of understanding affect, often unwittingly, the ways scientists design experiments, test theories, comprehend, communicate, and teach.
And why, while flawed, the study isn't meaningless or worthless - everyone's take is just lacking nuance:
Ariel M. (she/her)
Yes mostly. The paper (still a preprint, so not peer-reviewed) does start off kinda strong in its claims compared to what the results show. But they acknowledge all the limitations too: - this captured brief AI exposure, so did not look at how these effects change with usage over time
I am often told that public critique of published articles must also solve the issues found. I think this frequently enforced requirement hinders scientific self-correction. Blog post: mmmdata.io/posts/2025/07/critique-does-n…
I am disappointed by the fact that so many seem tempted to share a "nonrigorous", unserious attempt at quantifying fraud in science because it passes some silly vibe check ("I asked others and they agree"). So in the name of good science, we're ready to produce bad science as a rhetorical tool.
